Overview

Dataset statistics

Number of variables9
Number of observations100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory72.0 B

Variable types

DateTime1
Numeric8

Alerts

SYM/H_INDEX_nT is highly correlated with 1-M_AE_nT and 2 other fieldsHigh correlation
1-M_AE_nT is highly correlated with SYM/H_INDEX_nT and 1 other fieldsHigh correlation
400kmDensity is highly correlated with SYM/H_INDEX_nT and 5 other fieldsHigh correlation
DAILY_SUNSPOT_NO_ is highly correlated with 400kmDensity and 2 other fieldsHigh correlation
DAILY_F10.7_ is highly correlated with 400kmDensity and 2 other fieldsHigh correlation
3-H_KP*10_ is highly correlated with SYM/H_INDEX_nT and 2 other fieldsHigh correlation
irradiance (W/m^2/nm) is highly correlated with 400kmDensity and 2 other fieldsHigh correlation
d_diff is highly correlated with 400kmDensityHigh correlation
Datetime has unique values Unique
SYM/H_INDEX_nT has 2746 (2.7%) zeros Zeros
DAILY_SUNSPOT_NO_ has 24338 (24.3%) zeros Zeros
3-H_KP*10_ has 8469 (8.5%) zeros Zeros
d_diff has 1572 (1.6%) zeros Zeros

Reproduction

Analysis started2022-11-17 20:34:12.314495
Analysis finished2022-11-17 20:34:32.393985
Duration20.08 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Datetime
Date

UNIQUE

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2002-08-01 00:32:00
Maximum2012-06-30 23:54:00
2022-11-17T15:34:32.566888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:32.715442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SYM/H_INDEX_nT
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct320
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.42478
Minimum-451
Maximum101
Zeros2746
Zeros (%)2.7%
Negative76463
Negative (%)76.5%
Memory size781.4 KiB
2022-11-17T15:34:32.866966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-451
5-th percentile-41
Q1-18
median-8
Q3-1
95-th percentile9
Maximum101
Range552
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.40470065
Coefficient of variation (CV)-1.698474776
Kurtosis58.11383626
Mean-11.42478
Median Absolute Deviation (MAD)8
Skewness-4.641146228
Sum-1142478
Variance376.5424074
MonotonicityNot monotonic
2022-11-17T15:34:32.996705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-33565
 
3.6%
-23484
 
3.5%
-53479
 
3.5%
-73454
 
3.5%
-83406
 
3.4%
-43377
 
3.4%
-63305
 
3.3%
-13289
 
3.3%
-93268
 
3.3%
-103048
 
3.0%
Other values (310)66325
66.3%
ValueCountFrequency (%)
-4511
< 0.1%
-4462
< 0.1%
-4201
< 0.1%
-3981
< 0.1%
-3921
< 0.1%
-3771
< 0.1%
-3761
< 0.1%
-3741
< 0.1%
-3711
< 0.1%
-3681
< 0.1%
ValueCountFrequency (%)
1011
< 0.1%
861
< 0.1%
791
< 0.1%
781
< 0.1%
761
< 0.1%
751
< 0.1%
711
< 0.1%
651
< 0.1%
631
< 0.1%
611
< 0.1%

1-M_AE_nT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1430
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.03421
Minimum1
Maximum3415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2022-11-17T15:34:33.181823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q138
median87
Q3233
95-th percentile616
Maximum3415
Range3414
Interquartile range (IQR)195

Descriptive statistics

Standard deviation212.5372747
Coefficient of variation (CV)1.214261342
Kurtosis9.388712433
Mean175.03421
Median Absolute Deviation (MAD)61
Skewness2.465619817
Sum17503421
Variance45172.09312
MonotonicityNot monotonic
2022-11-17T15:34:33.301853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32936
 
0.9%
38933
 
0.9%
33930
 
0.9%
27925
 
0.9%
30919
 
0.9%
29914
 
0.9%
35905
 
0.9%
25904
 
0.9%
26895
 
0.9%
36890
 
0.9%
Other values (1420)90849
90.8%
ValueCountFrequency (%)
12
 
< 0.1%
219
 
< 0.1%
361
 
0.1%
4131
 
0.1%
5185
 
0.2%
6284
0.3%
7327
0.3%
8375
0.4%
9472
0.5%
10506
0.5%
ValueCountFrequency (%)
34151
< 0.1%
33381
< 0.1%
28471
< 0.1%
28191
< 0.1%
27471
< 0.1%
24671
< 0.1%
23571
< 0.1%
23041
< 0.1%
22841
< 0.1%
22801
< 0.1%

400kmDensity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99375
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.488857604 × 10-12
Minimum1.004137 × 10-15
Maximum2.409958 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2022-11-17T15:34:33.453350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.004137 × 10-15
5-th percentile2.0491354 × 10-13
Q14.98625825 × 10-13
median9.643517 × 10-13
Q31.93218925 × 10-12
95-th percentile4.63733015 × 10-12
Maximum2.409958 × 10-11
Range2.409857586 × 10-11
Interquartile range (IQR)1.433563425 × 10-12

Descriptive statistics

Standard deviation1.479053648 × 10-12
Coefficient of variation (CV)0.9934151151
Kurtosis0
Mean1.488857604 × 10-12
Median Absolute Deviation (MAD)5.7618255 × 10-13
Skewness0
Sum1.488857604 × 10-7
Variance2.187599694 × 10-24
MonotonicityNot monotonic
2022-11-17T15:34:33.664383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.063028 × 10-123
 
< 0.1%
2.148843 × 10-123
 
< 0.1%
1.013015 × 10-123
 
< 0.1%
1.374756 × 10-123
 
< 0.1%
1.800618 × 10-122
 
< 0.1%
1.387199 × 10-122
 
< 0.1%
1.722024 × 10-122
 
< 0.1%
1.444553 × 10-122
 
< 0.1%
1.136125 × 10-132
 
< 0.1%
1.448267 × 10-122
 
< 0.1%
Other values (99365)99976
> 99.9%
ValueCountFrequency (%)
1.004137 × 10-151
< 0.1%
1.739733 × 10-151
< 0.1%
3.272275 × 10-151
< 0.1%
4.086779 × 10-151
< 0.1%
4.178992 × 10-151
< 0.1%
4.24821 × 10-151
< 0.1%
4.338149 × 10-151
< 0.1%
4.40762 × 10-151
< 0.1%
5.317355 × 10-151
< 0.1%
6.15633 × 10-151
< 0.1%
ValueCountFrequency (%)
2.409958 × 10-111
< 0.1%
1.981068 × 10-111
< 0.1%
1.765326 × 10-111
< 0.1%
1.68191 × 10-111
< 0.1%
1.671561 × 10-111
< 0.1%
1.534461 × 10-111
< 0.1%
1.497177 × 10-111
< 0.1%
1.4876 × 10-111
< 0.1%
1.459356 × 10-111
< 0.1%
1.392578 × 10-111
< 0.1%

DAILY_SUNSPOT_NO_
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct214
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.67445
Minimum0
Maximum281
Zeros24338
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2022-11-17T15:34:33.803272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median31
Q373
95-th percentile149
Maximum281
Range281
Interquartile range (IQR)65

Descriptive statistics

Standard deviation50.63987748
Coefficient of variation (CV)1.062201609
Kurtosis1.428870838
Mean47.67445
Median Absolute Deviation (MAD)31
Skewness1.295382476
Sum4767445
Variance2564.397191
MonotonicityNot monotonic
2022-11-17T15:34:34.278338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024338
24.3%
132478
 
2.5%
122376
 
2.4%
151929
 
1.9%
141821
 
1.8%
181405
 
1.4%
261358
 
1.4%
161351
 
1.4%
111242
 
1.2%
231039
 
1.0%
Other values (204)60663
60.7%
ValueCountFrequency (%)
024338
24.3%
557
 
0.1%
6138
 
0.1%
7318
 
0.3%
8256
 
0.3%
9493
 
0.5%
10799
 
0.8%
111242
 
1.2%
122376
 
2.4%
132478
 
2.5%
ValueCountFrequency (%)
28127
 
< 0.1%
27928
 
< 0.1%
27039
< 0.1%
26730
< 0.1%
26337
< 0.1%
25240
< 0.1%
25074
0.1%
24859
0.1%
24755
0.1%
23930
< 0.1%

DAILY_F10.7_
Real number (ℝ≥0)

HIGH CORRELATION

Distinct927
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.696717
Minimum65.1
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2022-11-17T15:34:34.433915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum65.1
5-th percentile67.5
Q171.5
median85.2
Q3111.2
95-th percentile158.6
Maximum999.9
Range934.8
Interquartile range (IQR)39.7

Descriptive statistics

Standard deviation54.35243319
Coefficient of variation (CV)0.5563383792
Kurtosis184.3572119
Mean97.696717
Median Absolute Deviation (MAD)15.6
Skewness11.50422375
Sum9769671.7
Variance2954.186994
MonotonicityNot monotonic
2022-11-17T15:34:34.595871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.3834
 
0.8%
68743
 
0.7%
69.8714
 
0.7%
68.8705
 
0.7%
69.5680
 
0.7%
68.2641
 
0.6%
67.4639
 
0.6%
70.3634
 
0.6%
70.2631
 
0.6%
68.5618
 
0.6%
Other values (917)93161
93.2%
ValueCountFrequency (%)
65.125
 
< 0.1%
65.237
 
< 0.1%
65.538
 
< 0.1%
65.633
 
< 0.1%
65.867
 
0.1%
65.977
 
0.1%
6692
 
0.1%
66.1103
 
0.1%
66.2258
0.3%
66.3220
0.2%
ValueCountFrequency (%)
999.9246
0.2%
275.433
 
< 0.1%
270.931
 
< 0.1%
267.641
 
< 0.1%
25430
 
< 0.1%
246.939
 
< 0.1%
245.230
 
< 0.1%
242.634
 
< 0.1%
240.630
 
< 0.1%
232.835
 
< 0.1%

3-H_KP*10_
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.04697
Minimum0
Maximum90
Zeros8469
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2022-11-17T15:34:34.714296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median17
Q327
95-th percentile43
Maximum90
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.17234393
Coefficient of variation (CV)0.7853032352
Kurtosis0.7967219941
Mean18.04697
Median Absolute Deviation (MAD)10
Skewness0.9143439097
Sum1804697
Variance200.8553324
MonotonicityNot monotonic
2022-11-17T15:34:34.825094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
311361
11.4%
711179
11.2%
109455
9.5%
138566
8.6%
08469
8.5%
178157
8.2%
207392
7.4%
236501
 
6.5%
276071
 
6.1%
305329
 
5.3%
Other values (18)17520
17.5%
ValueCountFrequency (%)
08469
8.5%
311361
11.4%
711179
11.2%
109455
9.5%
138566
8.6%
178157
8.2%
207392
7.4%
236501
6.5%
276071
6.1%
305329
5.3%
ValueCountFrequency (%)
9013
 
< 0.1%
8743
 
< 0.1%
8350
 
0.1%
8021
 
< 0.1%
7771
 
0.1%
73113
 
0.1%
7092
 
0.1%
67104
 
0.1%
63174
0.2%
60296
0.3%

irradiance (W/m^2/nm)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3231
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005515761088
Minimum0.004873058293
Maximum0.007349349558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2022-11-17T15:34:34.962230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.004873058293
5-th percentile0.004923261702
Q10.005049338564
median0.005327608902
Q30.005854960997
95-th percentile0.006626430433
Maximum0.007349349558
Range0.002476291265
Interquartile range (IQR)0.0008056224324

Descriptive statistics

Standard deviation0.0005479276685
Coefficient of variation (CV)0.09933854272
Kurtosis0.1077951908
Mean0.005515761088
Median Absolute Deviation (MAD)0.0003593955189
Skewness0.9265807933
Sum551.5761088
Variance3.002247299 × 10-7
MonotonicityNot monotonic
2022-11-17T15:34:35.101401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00495163817108
 
0.1%
0.0049121202994
 
0.1%
0.00492521608285
 
0.1%
0.00491453474482
 
0.1%
0.00489577231976
 
0.1%
0.00493029179174
 
0.1%
0.0049805198872
 
0.1%
0.0051987683472
 
0.1%
0.00496965413972
 
0.1%
0.00565804960271
 
0.1%
Other values (3221)99194
99.2%
ValueCountFrequency (%)
0.00487305829327
< 0.1%
0.00487712817329
< 0.1%
0.00487718591528
< 0.1%
0.00487758824630
< 0.1%
0.00488132471215
< 0.1%
0.00488169817315
< 0.1%
0.00488175591537
< 0.1%
0.0048855622335
< 0.1%
0.00488571077629
< 0.1%
0.00488573964722
< 0.1%
ValueCountFrequency (%)
0.00734934955832
< 0.1%
0.0073424815236
< 0.1%
0.00733470916735
< 0.1%
0.00730189075735
< 0.1%
0.00726822437729
< 0.1%
0.00726604228833
< 0.1%
0.00725956214639
< 0.1%
0.00725760450636
< 0.1%
0.00724730687219
< 0.1%
0.00721854716523
< 0.1%

d_diff
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct96495
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.753678404 × 10-16
Minimum-1.1317823 × 10-11
Maximum7.0179446 × 10-12
Zeros1572
Zeros (%)1.6%
Negative48068
Negative (%)48.1%
Memory size781.4 KiB
2022-11-17T15:34:35.247833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1317823 × 10-11
5-th percentile-1.669487 × 10-13
Q1-3.603625 × 10-14
median4.405 × 10-16
Q33.793225 × 10-14
95-th percentile1.6105214 × 10-13
Maximum7.0179446 × 10-12
Range1.83357676 × 10-11
Interquartile range (IQR)7.39685 × 10-14

Descriptive statistics

Standard deviation1.788330009 × 10-13
Coefficient of variation (CV)-310.8150791
Kurtosis0
Mean-5.753678404 × 10-16
Median Absolute Deviation (MAD)3.69934 × 10-14
Skewness0
Sum-5.753678404 × 10-11
Variance3.19812422 × 10-26
MonotonicityNot monotonic
2022-11-17T15:34:35.388115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01572
 
1.6%
-3.9281 × 10-144
 
< 0.1%
3.7324 × 10-144
 
< 0.1%
4.4943 × 10-144
 
< 0.1%
4.2494 × 10-143
 
< 0.1%
-1.039 × 10-153
 
< 0.1%
-6.155 × 10-153
 
< 0.1%
2.2696 × 10-143
 
< 0.1%
-7.928 × 10-153
 
< 0.1%
5.02 × 10-163
 
< 0.1%
Other values (96485)98398
98.4%
ValueCountFrequency (%)
-1.1317823 × 10-111
< 0.1%
-6.4132083 × 10-121
< 0.1%
-6.3550716 × 10-121
< 0.1%
-5.724647 × 10-121
< 0.1%
-5.393795 × 10-121
< 0.1%
-5.2722229 × 10-121
< 0.1%
-4.9769291 × 10-121
< 0.1%
-4.695843 × 10-121
< 0.1%
-4.645975 × 10-121
< 0.1%
-4.521292 × 10-121
< 0.1%
ValueCountFrequency (%)
7.0179446 × 10-121
< 0.1%
5.45498 × 10-121
< 0.1%
3.871182 × 10-121
< 0.1%
3.78975651 × 10-121
< 0.1%
3.6282064 × 10-121
< 0.1%
3.5397972 × 10-121
< 0.1%
3.317901 × 10-121
< 0.1%
3.1141065 × 10-121
< 0.1%
3.07818 × 10-121
< 0.1%
3.059079 × 10-121
< 0.1%

Interactions

2022-11-17T15:34:30.522321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:22.181436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:23.200484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:24.278605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:25.600973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:26.781309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:28.302404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:29.468556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:30.663711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:22.321113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:23.326351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:24.476954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:25.742347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:26.914266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:28.465230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:29.599187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:30.800542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:22.452827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:23.458246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:24.647571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:25.904530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:27.069571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:28.616149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:29.734519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:30.939334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:22.578649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:23.581765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:24.825138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:26.055773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:27.547443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:28.765874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:29.871620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:31.089728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:22.701069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:23.710072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:24.988961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:26.198675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:27.706879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:28.914413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:30.005580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:31.241453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:22.824695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:23.839140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:25.141427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:26.344032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:27.863237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:29.063187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:30.134564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:31.405458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:22.943434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:23.975217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:25.284871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:26.482099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:28.004613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:29.198550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:30.260226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:31.560605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:23.068697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:24.118787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:25.439437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:26.631477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:28.149038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:29.332646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-17T15:34:30.388572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-17T15:34:35.516236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-17T15:34:35.680595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-17T15:34:35.896103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-17T15:34:36.063319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-17T15:34:36.237688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-17T15:34:31.721346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-17T15:34:32.122708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DatetimeSYM/H_INDEX_nT1-M_AE_nT400kmDensityDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_irradiance (W/m^2/nm)d_diff
02011-10-15 02:25:00-12.0178.04.789873e-12148.0136.933.00.006212-1.369510e-13
12006-11-01 00:43:00-21.0154.09.967436e-1353.085.317.00.005186-1.766994e-13
22004-08-29 19:58:0020.054.07.392175e-1327.087.713.00.0056063.271490e-14
32009-06-24 18:53:0042.074.04.133310e-1317.069.150.00.0049442.687410e-14
42011-11-07 11:11:004.026.07.050772e-12173.0178.97.00.006449-4.988700e-14
52003-11-21 03:36:00-186.0240.07.717244e-12119.0172.860.00.0063441.844890e-13
62008-04-19 03:57:002.0112.08.577820e-1310.071.717.00.0049544.133510e-14
72004-01-31 04:46:001.0101.01.303272e-1262.091.617.00.0056644.805200e-14
82005-11-02 01:42:00-20.0104.01.149596e-1229.076.817.00.0052001.030850e-13
92003-10-13 00:41:003.0206.01.121241e-1219.094.033.00.006050-2.467200e-14

Last rows

DatetimeSYM/H_INDEX_nT1-M_AE_nT400kmDensityDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_irradiance (W/m^2/nm)d_diff
999902007-09-05 07:59:00-17.0941.05.951310e-1313.068.833.00.004962-3.458970e-14
999912007-10-31 00:13:00-18.0129.05.022695e-130.066.113.00.0049442.386270e-14
999922009-09-02 23:03:00-8.018.02.494270e-130.069.410.00.0049852.276030e-14
999932004-07-08 13:23:006.057.06.032012e-1318.084.67.00.005514-3.677660e-14
999942007-12-18 15:18:00-31.0337.05.042662e-1312.074.433.00.004947-4.261840e-14
999952009-06-06 21:53:00-6.082.05.747234e-130.071.17.00.005003-3.026780e-14
999962011-04-20 06:36:00-18.066.03.751719e-1273.0118.137.00.0055923.491500e-14
999972004-07-04 04:16:00-10.0325.05.614132e-1331.082.113.00.005612-2.081170e-13
999982003-12-18 06:06:00-14.0139.08.519602e-13110.0119.110.00.005926-9.095700e-15
999992008-11-28 07:47:00-3.023.04.736454e-130.065.27.00.0049422.440020e-14